MINE 2.0: Enhanced biochemical coverage for peak identification in untargeted metabolomics

Jonathan Strutz; Kevin M. Shebek; Linda J. Broadbelt; Keith E.J. Tyo

doi:10.1093/bioinformatics/btac331

MINE 2.0: Enhanced biochemical coverage for peak identification in untargeted metabolomics

Jonathan Strutz, Kevin M. Shebek, Linda J. Broadbelt, Keith E.J. Tyo^*

^*Corresponding author for this work

Chemical and Biological Engineering

Research output: Contribution to journal › Article › peer-review

7 Scopus citations

Abstract

Summary: Although advances in untargeted metabolomics have made it possible to gather data on thousands of cellular metabolites in parallel, identification of novel metabolites from these datasets remains challenging. To address this need, Metabolic in silico Network Expansions (MINEs) were developed. A MINE is an expansion of known biochemistry which can be used as a list of potential structures for unannotated metabolomics peaks. Here, we present MINE 2.0, which utilizes a new set of biochemical transformation rules that covers 93% of MetaCyc reactions (compared to 25% in MINE 1.0). This results in a 17-fold increase in database size and a 40% increase in MINE database compounds matching unannotated peaks from an untargeted metabolomics dataset. MINE 2.0 is thus a significant improvement to this community resource.

Original language	English (US)
Pages (from-to)	3484-3487
Number of pages	4
Journal	Bioinformatics
Volume	38
Issue number	13
DOIs	https://doi.org/10.1093/bioinformatics/btac331
State	Published - Jul 1 2022

Funding

ASJC Scopus subject areas

Computational Mathematics
Molecular Biology
Biochemistry
Statistics and Probability
Computer Science Applications
Computational Theory and Mathematics

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10.1093/bioinformatics/btac331

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title = "MINE 2.0: Enhanced biochemical coverage for peak identification in untargeted metabolomics",

abstract = "Summary: Although advances in untargeted metabolomics have made it possible to gather data on thousands of cellular metabolites in parallel, identification of novel metabolites from these datasets remains challenging. To address this need, Metabolic in silico Network Expansions (MINEs) were developed. A MINE is an expansion of known biochemistry which can be used as a list of potential structures for unannotated metabolomics peaks. Here, we present MINE 2.0, which utilizes a new set of biochemical transformation rules that covers 93% of MetaCyc reactions (compared to 25% in MINE 1.0). This results in a 17-fold increase in database size and a 40% increase in MINE database compounds matching unannotated peaks from an untargeted metabolomics dataset. MINE 2.0 is thus a significant improvement to this community resource.",

author = "Jonathan Strutz and Shebek, {Kevin M.} and Broadbelt, {Linda J.} and Tyo, {Keith E.J.}",

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doi = "10.1093/bioinformatics/btac331",

language = "English (US)",

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issn = "1367-4803",

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TY - JOUR

T1 - MINE 2.0

T2 - Enhanced biochemical coverage for peak identification in untargeted metabolomics

AU - Strutz, Jonathan

AU - Shebek, Kevin M.

AU - Broadbelt, Linda J.

AU - Tyo, Keith E.J.

PY - 2022/7/1

Y1 - 2022/7/1

N2 - Summary: Although advances in untargeted metabolomics have made it possible to gather data on thousands of cellular metabolites in parallel, identification of novel metabolites from these datasets remains challenging. To address this need, Metabolic in silico Network Expansions (MINEs) were developed. A MINE is an expansion of known biochemistry which can be used as a list of potential structures for unannotated metabolomics peaks. Here, we present MINE 2.0, which utilizes a new set of biochemical transformation rules that covers 93% of MetaCyc reactions (compared to 25% in MINE 1.0). This results in a 17-fold increase in database size and a 40% increase in MINE database compounds matching unannotated peaks from an untargeted metabolomics dataset. MINE 2.0 is thus a significant improvement to this community resource.

AB - Summary: Although advances in untargeted metabolomics have made it possible to gather data on thousands of cellular metabolites in parallel, identification of novel metabolites from these datasets remains challenging. To address this need, Metabolic in silico Network Expansions (MINEs) were developed. A MINE is an expansion of known biochemistry which can be used as a list of potential structures for unannotated metabolomics peaks. Here, we present MINE 2.0, which utilizes a new set of biochemical transformation rules that covers 93% of MetaCyc reactions (compared to 25% in MINE 1.0). This results in a 17-fold increase in database size and a 40% increase in MINE database compounds matching unannotated peaks from an untargeted metabolomics dataset. MINE 2.0 is thus a significant improvement to this community resource.

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MINE 2.0: Enhanced biochemical coverage for peak identification in untargeted metabolomics

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